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NEW QUESTION # 73
You have a table in a Fabric lakehouse that contains the following data.
You have a notebook that contains the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 74
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on-premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?
Answer: A
Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Fabric, a data pipeline is the most appropriate tool. A data pipeline in Fabric is designed to move data between various data sources and destinations, including on-premises databases like SQL Server, and cloud-based storage like Fabric warehouses. The data pipeline can handle the connection through an on-premises data gateway, which is required to access on-premises data. This solution facilitates the orchestration of data movement and transformations if needed.
NEW QUESTION # 75
You have a Fabric warehouse named Warehouse1 that contains a table named Table1. Table1 contains the following columns that store string values:
* FirstName
* Phone
* Email
You have two Microsoft Entra security groups named Group1 and Group2. The members of both groups can query Table1 and see full values for FirstName, Phone, and Email.
You need to configure Table1 to meet the following requirements:
. The members of Group1 must see masked values for Phone, showing only the last three characters of each phone number.
. The Group1 members must see unmasked values for FirstName.
. The members of Group2 must see unmasked values for all the columns.
. The solution must use Fabric warehouse-supported permissions and T-SQL.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 76
You have three users named User1, User2, and User3.
You have the Fabric workspaces shown in the following table.
You have a security group named Group1 that contains User1 and User3.
The Fabric admin creates the domains shown in the following table.
User1 creates a new workspace named Workspace3.
You add Group1 to the default domain of Domain1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 77
You have a Fabric workspace named WS1 that contains several scheduled data pipelines. WS1 has workspace monitoring enabled, and the monitoring eventhouse contains a table named ItemJobEventLogs.
Users report that some downstream reports are missing newly ingested data.
You need to identify failed pipeline runs from the last nine minutes and return only the columns needed for troubleshooting.
How should you complete the KQL query?
Answer:
Explanation:
Explanation:
First selection: JobType == ' Pipeline '
Second selection: SecondsAgo < = 540
Completed KQL query:
ItemJobEventLogs
| where JobType == ' Pipeline ' and JobStatus == ' Failed '
| extend SecondsAgo = datetime_diff( ' second ' , now(), ingestion_time())
| where SecondsAgo < = 540
| order by Timestamp desc
| project Timestamp, ItemName, WorkspaceName, JobStartTime, JobEndTime, JobStatus The correct selections are JobType == ' Pipeline ' and SecondsAgo < = 540 . Microsoft Fabric workspace monitoring writes item-level execution information to the ItemJobEventLogs table in the monitoring Eventhouse. Microsoft provides this specific KQL pattern for detecting recent pipeline failures , using JobType == ' Pipeline ' together with JobStatus == ' Failed ' .
The query then calculates:
datetime_diff( ' second ' , now(), ingestion_time())
This produces the approximate number of seconds elapsed since each monitoring record was ingested.
Because nine minutes = 540 seconds , the predicate SecondsAgo < = 540 retains records ingested during the preceding nine-minute window. SecondsAgo > = 540 would do the opposite by retaining records at least nine minutes old.
JobEndTime > = ago(9m) is not the correct selection because the query explicitly derives ingestion recency through ingestion_time(). Microsoft's documented workspace-monitoring example uses the same SecondsAgo < = 540 pattern.
Finally, project limits the result to the operational fields required for troubleshooting: timestamp, pipeline name, workspace, start/end times, and failure status.
Study Guide reference: Monitor and optimize an analytics solution # workspace monitoring # Eventhouse monitoring logs # KQL analysis of pipeline execution and failures.
NEW QUESTION # 78
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